A method for preventing denial of service attacks which are distributed attacks is applied in a target service provider server, a platform server, and a botnet service provider server. The target service provider server determines a first SDN controller according to an attack protection request, and issues a first flow rule. The target service provider server directs data flow of a network equipment to a first cleaning center and controls the first cleaning center to identify the attacking or malicious element in the data flow according to the first flow rule. The platform server receives the attacking element in the data flow sent by the target service provider server, and regards the same as malicious traffic. The platform server generates an attack report, and sends the attack report to the botnet service provider server to notify the botnet service provider server to clean or filter out the malicious traffic.

Various embodiments of an apparatus, method(s), system(s) and computer program product(s) described herein are directed to an Archiving Engine that detects a regulated user account(s) joining a virtual meeting and instantiating a virtual meeting participant instance to capturing one or more communication channels of the virtual meeting hosted by a communication system. The Archiving Engine generates an archival file(s) based on the captured communication channel data. The Archiving Engine generates one or more translated files by applying a compliance policy associated with at least one of the regulated user accounts to the one or more archival files.

A system and method for detecting bots. The method includes receiving a request to access a server, the request is being received from a client device, and responsive to the request, causing the client device to download a script code file to the client device. The script code file, when executed, collects a profile, and the profile includes a plurality of parameters. The method also includes receiving the created profile, generating a score based on the plurality of parameters to identify a bot, and initiating a mitigation action based on the identified bot.

A method of detecting blockchain miner code executing in a web browser including receiving a profile for the browser identifying typical resource consumption by the browser in use; responsive to a detection of a deviation of the resource consumption by the browser from the profile, intercepting a communication with the browser including a cryptographic nonce, training a plurality of classifiers based on generated training examples, each training example being generated by applying a hashing algorithm to the nonce such that each classifier is trained with training examples generated using a different hashing algorithm; intercepting one or more second communications with the browser, each of the second communications including a hash value; executing at least a subset of the classifiers based on the hash value of each of the second communications; and identifying malicious miner code executing in the browser.

A method of anomaly detection for network traffic communicated by devices via a computer network, the method including receiving a set of training time series each including a plurality of time windows of data corresponding to network communication characteristics for a first device; training an autoencoder for a first cluster based on a time series in the first cluster, wherein a state of the autoencoder is periodically recorded after a predetermined fixed number of training examples to define a set of trained autoencoders for the first cluster; receiving a new time series including a plurality of time windows of data corresponding to network communication characteristics for the first device; for each time window of the new time series, generating a vector of reconstruction errors for the first device for each autoencoder based on testing the autoencoder with data from the time window; and evaluating a derivative of each vector; training a machine learning model based on the derivatives so as to define a filter for identifying subsequent time series for a second device being absent anomalous communication.

In one embodiment, a service computes a plurality of features of a subdomain for which a Domain Name System (DNS) query was issued. The service aggregates the plurality of computed features into a feature vector. The service uses the feature vector as input to a machine learning classifier, to determine whether the subdomain is a DNS tunneling domain name. The service provides an indication that the subdomain is a DNS tunneling domain name, when the machine learning classifier determines that the subdomain is a DNS tunneling domain name.

A computer system is provided. The computer system includes a memory, a network interface, and a processor coupled to the memory and the network interface. The processor is configured to receive a response to a request to verify whether an ostensible client of a service is actually a client or a bot, the response including an indicator of whether the ostensible client is a client or a bot; receive information descriptive of interoperations between the ostensible client and the service that are indicative of whether the ostensible client is a client or a bot; and train a plurality of machine learning classifiers using the information and the indicator to generate a next generation of the plurality of machine learning classifiers.

A system and computer-implemented method of monitoring a network is provided. The method includes receiving a packet of network traffic, wherein the packet has an associated source and destination address pair, where this pair constitutes a connection pair. The method further includes comparing the packet to a plurality of patterns and/or compare a source or destination address of the packet to known malicious addresses, and upon determining that the packet matches a pattern of the plurality of patterns or the source or destination address of the packet matches a known malicious address. The method further includes deploying a honeypot in a container for the pattern matching the packet, if not yet deployed, and forwarding all network traffic for the connection pair to the honeypot.

The present disclosure is directed to a method of detecting anomalous behaviors based on a temporal profile. The method can include collecting, by a control system comprising a processor and memory, a set of network data communicated by a plurality of network nodes over a network during a time duration. The method can include identifying, by the control system, one or more seasonalities from the set of network data. The method can include generating, by the control system, a temporal profile based on the one or more identified seasonalities. The method can include detecting, by the control system and based on the temporal profile, an anomalous behavior performed by one of the plurality of network nodes. The method can include identifying, by the control system and based on the temporal profile, a root cause for the anomalous behavior.

A network security computing system includes a steganographic communications analysis engine monitoring incoming and outgoing messages on a secure computing network. The steganographic communications analysis engine identifies a pattern of file transfers between a first computing device on the secure computing network and an internal or external message recipient. When a pattern is identified, the steganographic communications analysis engine quarantines an associated computing device from the secure network. The steganographic communications analysis engine analyzes files transferred between the computing device and the recipient for indications of steganographic information and causes display, based on an identified indication of steganography, an indication that the computing device had been compromised by command and control malware.